Key Takeaways
- A location intelligence stack consists of five layers: data, storage and compute, integration, analysis, and delivery.
- Reference data (places, building footprints, addresses) should go in before mobility or demographic data, because every behavioral dataset inherits the errors of the place layer.
- 43% of chief operations officers name data quality as their most significant data priority, according to a 2025 IBM Institute for Business Value report.
- Polygons assign visits and transactions to the right tenant in shared buildings, which radius buffers cannot do.
- No single platform is best. The right tools depend on whether your team works in SQL, GIS, BI, or Python.
Try out this small experiment at work this week. Go to five different teams and ask them how many coffee shops are there on one particular block, and see if even two of them give you the same number. Your takeaway: Nobody has planned the location intelligence stack yet.
It rarely gets planned. Someone buys a mapping tool and then a year later a new project needs a dataset, so that gets added, and then another system after it and so on. Each choice made sense on the day but together, they don’t agree with each other.
This article goes layer by layer, and we’ve also marked where stacks usually break. If you are someone who’s new to all this, don’t worry; we have built a seven-step build order near the end, and after reading the blog, it will make perfect sense to you.
The Five Layers Of A Location Intelligence Stack
When we say location intelligence stack, we simply mean everything your company uses to answer questions that have a “where” in them, so the data, the tools, the platforms, and the workflows all put together, questions like:
- Where to open the next location?
- Which markets should we enter?
- Where are our best customers?
- Which areas represent the biggest growth opportunities?
A location intelligence stack comprises five different layers, each one feeding the next. If we skip one layer, then the layers above it will get messy.
Layer | What it does | Typical tools |
Data | Supplies the facts about places, people, and your own business | POI, building footprint, address, demographic, and mobility datasets, plus first-party records |
Storage and compute | Holds the data and runs spatial queries at scale | Snowflake, Databricks, BigQuery, PostGIS |
Integration | Cleans, matches, and joins datasets | dbt, Airflow, geocoders, H3 indexing, Placekey |
Analysis | Answers the spatial question | ArcGIS, QGIS, CARTO, Python with GeoPandas |
Delivery | Puts answers in front of decision makers | Tableau, Power BI, kepler.gl, internal apps |
Two research firms estimated the size of this market for 2025, but their numbers are more than five billion dollars apart.
Firm | 2025 market size | Forecast |
USD 20.90 billion | USD 66.12 billion by 2034 | |
USD 26.15 billion | USD 88.12 billion by 2033 |
That’s a huge gap, because each firm draws its own line around what counts as location intelligence for them in particular. Both expect strong growth, and that is about all you should read into either number.
Neither report tells you why one project works and the other fails. What we have seen is that it always comes down to the data layer. Picture a store that closed last spring but is still sitting in your dataset. No location intelligence software will catch that for you.
So, what do you get once all the layers line up? Three things change:
- Nobody needs to clean the same store list twice, so decisions move faster.
- Fewer arguments about whose numbers are right, as they all read one number.
- The new question becomes cheaper to answer, as the joins already exist.
Which Data Sources Are Essential For A Location Intelligence Stack?
Six kinds of data show up in every stack. Each one answers a different question.
Data type | Question it answers | Common source | What to watch |
Points of interest | What is at this location? | SafeGraph Places | Stale records, duplicates |
Building footprints | Where do a place’s boundaries sit? | SafeGraph Geometry | Pins without shapes |
Addresses | Is this address real, and where is it? | SafeGraph Address | Formats that differ by country |
Demographics | Who lives nearby? | US Census Bureau and other national statistics offices | Release lag |
Mobility | How do people move through an area? | Specialist mobility providers | Privacy rules, panel bias |
First-party | How is our own business doing? | CRM, POS, store lists | Messy, hand-typed addresses |
Out of the six, the first three are the ones which SafeGraph builds and together they build the reference layer
Product | What you get | Good to know |
80M+ POIs across 195+ countries and territories | 15K+ brands, 900+ categories, refreshed monthly | |
Polygons in Well-Known Text (WKT) format with metadata | Spatial hierarchies, parking attributes, polygon quality indicators | |
Structured address records with lat/long | Built for analytics, routing, geospatial applications |
Note: One boundary is worth stating plainly. SafeGraph Places deals with physical locations only, not people. It does not include any data related to individual consumers, device signals, or foot traffic information of any kind.
And what if your project does need movement data? Then you will have to get it from a mobility data vendor and join it to the place layer yourself.
What Goes First: POI, Mobility, Demographic, or Geospatial Data
Most people will tell you to begin with the richest dataset you can find, and usually what they mean by that is mobility. We would argue for the opposite order. Every behavioral dataset inherits the errors of the place layer beneath it, so the reference data goes in first.
Think of a strip mall you have been to, the kind where a pharmacy, a gym and a taco shop are all using the same parking lot and even the same street address. If all you have is pins, a visit or a card payment just goes to whichever pin is sitting closest, and that could easily be the wrong shop. Give the same data polygons and a parent-child hierarchy, and now that visit goes to the tenant it actually belongs to.
Here is a join order that works for most teams:
Step | What to do | Why |
1 | Standardize and geocode first-party addresses | Clean input for matching |
2 | Match each record to a POI, store a persistent ID such as Placekey | No more matching on business names |
3 | Attach polygons, assign point data (visits, deliveries, transactions) to a shape | More accurate than a radius |
4 | Roll up to census block groups or H3 hexagons | Demographics join at that level |
5 | Stamp every table with its data release | Explains why outputs changed between June and July |
Location Intelligence Tools And Platforms: Picking What Fits Your Team
We wish we could name one best platform and be done with it, but there isn’t one. What works for you depends mostly on where your team is already doing its work.
Team | Tools | Why |
SQL-first data teams | Snowflake, BigQuery, or Databricks, plus dbt | Native geospatial functions |
GIS analysts | ArcGIS or QGIS | Cartography, editing, deeper spatial methods |
Business users | CARTO, Tableau, or Power BI | A map is one chart among many |
Data scientists | Python with GeoPandas and H3 | Notebook work |
And what happens when the data gets really big? When your tables cross hundreds of millions of rows, it is better to run the spatial joins inside the warehouse itself and use H3 for indexing. Desktop GIS struggles at that size. That doesn’t mean you throw it away though, because for a one-off investigation, or for a map that someone outside the data team has to read, desktop GIS is still easier, and this is why you will find both in most mature stacks.
Before you sign up for any location intelligence software, we’d suggest asking two things.
First, can it read straight from your warehouse, or does it want its own copy of the data? And second, who in your team is really going to open this tool every week?
One more thing: whatever tools you end up with, please don’t keep copying data from one to another. This is also why SafeGraph delivers into Snowflake, AWS Marketplace, Databricks, CARTO and Esri ArcGIS, because then every team works from the same release. You can see the full list of integrations here.
APIs or Bulk Delivery: How Should Location Data Enter Your Workflows?
Most stacks use both, each for a different job.
Method | Best for | Trade-off |
API calls | Real-time lookups inside a product: address entry, store finders, enrichment at the point of capture | Per-call cost and rate limits; awkward for analysis across millions of rows |
Bulk files or cloud shares | Analytics, modeling, and machine learning features | You own the refresh pipeline |
Marketplace listings | Fast trials with no engineering work | Less control over schema and schedule |
So do you have to pick one? Not really. What a lot of teams do is load the bulk file into the warehouse and do all their modeling there, and they call the API only at the moment when a user is typing an address or searching for a store. Since both are pointing at the same place IDs, what the product shows and what the analysis says stay in line with each other.
Good habits which pay off regardless of whichever route you take:
Habit | What it protects |
Store the persistent place ID with every record | Matching later |
Check row counts, null rates, category mix on each load | Catching a bad release early |
Keep several past releases | Rebuilding historical analysis |
Record which release fed which dashboard or model | Tracing a changed number |
A Location Intelligence Stack For Retail Site Selection And Real Estate
Site selection is the classic test because it uses every layer. It is also where location intelligence for real estate earns its budget.
Take Avison Young as an example. Their commercial real estate clients came to them asking where an office, an industrial site or a store should go, and that one question quickly turns into several smaller ones, like how close is the highway or transit, where will people get lunch, and can the target consumers reach it. Avison Young used SafeGraph data to answer these ground-level questions.
A working site selection stack usually contains:
- Places for competitors, complementary brands, and the opened and closed dates that reveal churn along a corridor
- Geometry for building shape, parking, and shared-building relationships
- Census demographics for population and income by block group
- Mobility data from a specialist provider to draw realistic catchments
- First-party sales to calibrate the model against stores you already run
The workflow itself is short. Define the trade area, score each candidate, compare it with your best-performing existing stores, then re-score every month as places open and close.
Be honest about the limits, too. In brand-new developments, both POI and census data trail reality, and SafeGraph’s open and closed tracking starts in July 2019. A site visit still belongs in the process.
How Do You Build A Location Intelligence Stack From Scratch?
- Name three decisions: Write down the specific choices the stack must support. “Better insights” is not one of them.
- Audit first-party data: Count how many of your location records have a clean, geocodable address.
- Choose reference data: Before paying for any location intelligence data, ask for a sample and check it against a market that you personally know well. If you want to know what exactly to measure, we have a POI data quality checklist, and there is also a location data buyer’s guide if you are comparing where to source from.
- Stick with the warehouse you already have: A new platform is rarely the missing piece.
- Build the joins: Settle the ID strategy, the shared geography, and the release stamps.
- Add behavior and demographics last: add only the layers your three decisions need.
- Deliver and assign an owner: Put outputs where decisions happen, set a refresh cadence, and name the person responsible.
Tooling choices come late in this order on purpose. When a team picks the platform first, slowly its questions start fitting whatever the platform can draw, and the decisions it wrote down in step one are forgotten.
Common Location Intelligence Challenges And Their Fixes
Data quality is the recurring theme, and it is not unique to location. A 2025 report from the IBM Institute for Business Value found that 43% of chief operations officers call data quality their most significant data priority. IBM’s summary of that report adds that more than a quarter of organizations estimate losses above USD 5 million a year from poor data. Demand Gen Report’s 2026 Database Strategies & Contact Acquisition Benchmark Survey, which covers marketing contact databases and not location data, found that 61% of organizations still cleanse data by hand.
Location data adds its own twists.
Challenge | How it shows up | Fix |
Stale places | Closed stores inflate competitor counts | A monthly-updated source with opened and closed dates |
Entity mismatch | One store, three spellings, three records | Persistent place IDs and standardized addresses |
Misattributed points | Events credited to the business next door | Polygons instead of radius buffers |
Tool sprawl | Each team keeps its own copy | One governed copy in the warehouse |
Privacy uncertainty | Legal review stalls the project | Separate place data (no personal information) from people-based data; vet mobility vendor sourcing |
No owner | Refreshes quietly stop | A named owner and automated load checks |
The benefits from location intelligence only appear when the layers agree with each other. Look back at that table, and you will see most of the fixes are choices about data. Very few are about software.
Why The Data Layer Deserves Your First Dollar
Whatever model or dashboard you build, in the end it all comes down to one simple question, which is whether the list of real-world places under it is correct or not. If it is, you can trust your location-based intelligence, whether that is a trade area model or a slide for the board. And if it isn’t correct, the mistake quietly moves up through every layer, and the worst part is nobody notices because the chart on top still looks fine.
That list is what SafeGraph works on. With Places you get 80M+ points of interest, already cleaned and deduplicated, with a new release each month. Geometry shows where each place starts and stops. Address helps your own records match up. Places and Geometry can land directly in the cloud environment your team already uses, and for Address our team will walk you through the delivery options. Your analysts get to spend their day on the question.
Curious how your current site criteria hold up?
Download a free SafeGraph data sample including a Miami-Dade retail brands dataset, and run a couple of these checks on a market you know well.
FAQ’s
1. We’re a small chain with under 20 stores. What’s the best place to begin?
Begin with the data you already have on hand, like your store addresses, how much each location sells, and when each one opened. Add free Census demographics and a POI dataset covering competitors and neighbors, and you have enough to compare your best and worst stores and see what separates them. Mobility data and predictive models can wait until you’re evaluating several new sites a year.
2. What’s the best way to judge the top growth intelligence providers for retail store site selection?
Pick a market you know really well, like your home city, and ask every provider for a sample of that area. Then check their data against what you know is actually there. Did they catch the shop that closed last month? Do they get the store edges right in a crowded strip mall? Are the business categories detailed enough for what you sell? And can your team load the data into the tools they already use, without a lot of extra work?
3. How often should we refresh our retail location analysis?
Place data should update at least monthly, since openings and closures reshape trade areas all the time. A full trade area and competitor review makes sense once a year per store, and again whenever there’s a lease renewal, a major competitor opening, or a road or transit change nearby.
4. Can I use location data to forecast sales at a new store?
Yes, if you already have a good number of similar stores to compare against. Most teams do this by finding existing stores that look like the new site, or by modeling how far people will travel to reach it. The forecast gets shakier if the format is new, the region is new, or you only have a few stores to learn from. So think of the number as a rough range, and once the store opens, check how close you got.
5. Does using location data for retail raise privacy concerns?
It depends on what kind of data you mean. Place data is about businesses, like where a store is, what it sells, and the shape of its building, so it doesn’t track anyone. Mobility data is different because it comes from people’s phones. That’s why it should only come from people who agreed to share it, and it should be grouped so no single person can be picked out. Before you buy anything, ask the provider where their data comes from.
